Returns a tensor filled with random numbers from a normal distribution with mean 0 and variance 1 (also called the standard normal distribution).
out i ∼ N ( 0 , 1 ) \text{out}_{i} \sim \mathcal{N}(0, 1) outi∼N(0,1)
For complex dtypes, the tensor is i.i.d. sampled from a complex normal distribution with zero mean and unit variance as
out i ∼ C N ( 0 , 1 ) \text{out}_{i} \sim \mathcal{CN}(0, 1) outi∼CN(0,1)
This is equivalent to separately sampling the real ( Re ) (\operatorname{Re}) (Re) and imaginary ( Im ) (\operatorname{Im}) (Im) part of out i \text{out}_i outi as
Re ( out i ) ∼ N ( 0 , 1 2 ) , Im ( out i ) ∼ N ( 0 , 1 2 ) \operatorname{Re}(\text{out}_{i}) \sim \mathcal{N}(0, \frac{1}{2}),\quad \operatorname{Im}(\text{out}_{i}) \sim \mathcal{N}(0, \frac{1}{2}) Re(outi)∼N(0,21),Im(outi)∼N(0,21)
The shape of the tensor is defined by the variable argument size.
size (int...) – a sequence of integers defining the shape of the output tensor. Can be a variable number of arguments or a collection like a list or tuple.
generator (torch.Generator, optional) – a pseudorandom number generator for sampling
out (Tensor, optional) – the output tensor.
dtype (torch.dtype, optional) – the desired data type of returned tensor. Default: if None, uses a global default (see torch.set_default_dtype()).
layout (torch.layout, optional) – the desired layout of returned Tensor. Default: torch.strided.
device (torch.device, optional) – the desired device of returned tensor. Default: if None, uses the current device for the default tensor type (see torch.set_default_device()). device will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types.
requires_grad (bool, optional) – If autograd should record operations on the returned tensor. Default: False.
pin_memory (bool, optional) – If set, returned tensor would be allocated in the pinned memory. Works only for CPU tensors. Default: False.
Example:
>>> torch.randn(4)
tensor([-2.1436, 0.9966, 2.3426, -0.6366])
>>> torch.randn(2, 3)
tensor([[ 1.5954, 2.8929, -1.0923],
[ 1.1719, -0.4709, -0.1996]])
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